# Aligning ESG Controversy Data with International Guidelines through Semi-Automatic Ontology Construction

arXiv: https://arxiv.org/abs/2509.10922 · HTML: https://mlsanity.com/paper/2509.10922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 13 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Prediction of ESG Compliance using a Heterogeneous Information Network](https://mlsanity.com/paper/1811.12166/) (2018)
- [ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis](https://mlsanity.com/paper/2501.15720/) (2025)
- [Hierarchical Narrative Analysis: Unraveling Perceptions of Generative AI](https://mlsanity.com/paper/2409.11032/) (2024)

## Abstract (arXiv)

The growing importance of environmental, social, and governance data in regulatory and investment contexts has increased the need for accurate, interpretable, and internationally aligned representations of non-financial risks, particularly those reported in unstructured news sources. However, aligning such controversy-related data with principle-based normative frameworks, such as the United Nations Global Compact or Sustainable Development Goals, presents significant challenges. These frameworks are typically expressed in abstract language, lack standardized taxonomies, and differ from the proprietary classification systems used by commercial data providers. In this paper, we present a semi-automatic method for constructing structured knowledge representations of environmental, social, and governance events reported in the news. Our approach uses lightweight ontology design, formal pattern modeling, and large language models to convert normative principles into reusable templates expressed in the Resource Description Framework. These templates are used to extract relevant information from news content and populate a structured knowledge graph that links reported incidents to specific framework principles. The result is a scalable and transparent framework for identifying and interpreting non-compliance with international sustainability guidelines.